This moment has finally come – we’re dedicating an entire article to robotics, the embodied entities of physical AI that exist in the real world. They move AI from digital space into physical environments, where machines can perceive, move, and act in real time. This means one key thing: robots now interact with the world on an entirely new level. And the big players – NVIDIA, Tesla, and even OpenAI (whose Sora 2 app is a big step forward) – are racing to master physical AI.

We’ve avoided this topic for a while because the technology stack behind robotics mostly includes vision-language-action (VLA) models, computer vision, world models, and mechanical systems – areas we’ve covered in other articles. Plus, robotics often overlaps with our Agentic AI series. Still, we can’t ignore that robotics is becoming its own field – not just about agentic behavior, vision, or physics, but everything combined. All these puzzle pieces come together to shape robots into what may soon become our everyday companions. How long will it take before living alongside robots feels normal?

We don’t know yet – but recent progress is clearly bringing that future closer. From Figure 03 and 1X’s Neo to Unitree’s quadrupeds and NVIDIA’s latest innovations, robotics is advancing faster than ever. In this article, we’ll explore the key technologies driving this shift with illustrative technical details to the essential concepts behind creating modern robotic systems. It’s a must read for everyone!

In today’s episode, we will cover:

Basic Terms to Become Fluent with Robotics

Robot Types

Terms are the base of every domain. If you’ve never dived deep into robotics before, this section may help you understand the subject. Today we can identify several main types of robots:

Machine learning (ML) and AI today enriches these entities with new perception, understanding and action capabilities. No matter if a robot is a humanoid, drone, or manipulator, it needs four main pillars to function meaningfully.

Four Main Pillars of Robotics

Perception helps the robot see, hear, and sense its environment. Its main components include:

AI models here help to interpret robot “senses.” They converts raw sensor data into a symbolic or numerical understanding of the world, handle object recognition, segmentation, motion detection, etc.

SLAM (Simultaneous Localization and Mapping) does two things at once:

It uses algorithms such as Extended Kalman Filters, Graph-SLAM, or particle filters to align sensor readings over time and minimize positional drift.

This covers how a robot acts physically, including locomotion, manipulation and whole-body control.

Locomotion means how a robot moves through its environment. It combines mechanics (hardware) and AI-based control (software).

Manipulation is about robot’s interaction with and moving objects. AI systems here are needed to provide visual and tactile feedback for adaptive grasping, language-guided manipulation (like “pick up the write cup”), and to predict object dynamics (for example, how items move or fall when pushed).

Mobile manipulation combines both and demonstrates more complex cases like ones when a robot moves through a room and picks up objects.

Whole-body control relates to the movement of all parts and joints of a robot.

In general, there are two broad types of methods for generating robot motion:

Many modern robotic systems combine both ideas.

This is the robot’s “thinking system”. Depending on the robot’s functioning, different AI models (models with RL, world models, transformers, neural-symbolic planners) can be used to choose actions, search and plan paths and high-level tasks, and react to new information. These models use search algorithms, learn from feedback, combine symbolic reasoning and neural decision policies, etc. to plan and execute high-level tasks, balancing goals, safety, and ethical constraints.

Optional but common add-ons may include:

One of the main shifts today happens in robot learning. Recently Hugging Face published “Robot Learning: A Tutorial” where they show how robotics learning approach moved from traditional dynamics-based models – mathematical equations describing how forces and motions behave – to ML methods that have started to transform how robots plan and act. Let’s look a little bit closer on general trends in robot learning.

How Robots Learn Today